Guineng Zheng
PhD Student. Research Interest: knowledge base construction and application.

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  • OpenTag: Open Attribute Value Extraction from Product Profiles
    By Guineng Zheng,    Subhabrata Mukherjee,    Xin Luna Dong,    Feifei Li
    (To Appear) In Proceedings of In Proceedings of 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD 2018),  pages ??-??,  August,  2018.

    Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted features. How can we discover new attribute values that we have never seen before? Can we do this with limited human annotation or supervision? We study this problem in the context of product catalogs that often have missing values for many attributes of interest.

    We leverage product profile information such as titles and descriptions to discover missing values of product attributes. We develop a novel deep tagging model OpenTag for this extraction problem with the following contributions: (1) we formalize the problem as a sequence tagging task, and propose a joint model exploiting recurrent neural networks (specifically, bidirectional LSTM) to capture context and semantics, and Conditional Random Fields (CRF) to enforce tagging consistency; (2) we develop a novel attention mechanism to provide interpretable explanation for our model’s decisions; (3) we propose a novel sampling strategy exploring active learning to reduce the burden of human annotation.OpenTag does not use any dictionary or hand-crafted features as in prior works. Extensive experiments in real-life datasets show that OpenTag with our active learning strategy discovers new attribute values from as few as 150 annotated samples (reduction in 3.3x amount of annotation effort) with a high f-score of 83%, outperforming state-of-the-art models.

  • 2017

  • DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
    By Min Du,    Feifei Li,    Guineng Zheng,    Vivek Srikumar
    In Proceedings of 24th ACM Conference on Computer and Communications Security (CCS 2017),  pages 1285--1298,  November,  2017.

    Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data are universally available in nearly all computer systems. Therefore, log data is an important and valuable data source for understanding system status and performance issues, which means various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution. In addition, we demonstrate how to incrementally update the DeepLog model in an online fashion so that it can adapt to new log patterns over time. Furthermore, DeepLog constructs workflows from the underlying system log so that once an anomaly is detected, users can diagnose the detected anomaly and perform root cause analysis effectively. Extensive experimental evaluations over large log data have shown that DeepLog has outperformed other existing log-based anomaly detection methods based on traditional data mining methodologies.